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A novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.
Version: | 0.1.0 |
Depends: | R (≥ 3.5) |
Imports: | purrr, pracma, tidyr, dplyr, ggplot2, gridExtra |
Published: | 2024-02-01 |
DOI: | 10.32614/CRAN.package.rbbnp |
Author: | Xinyu DAI [aut, cre], Susanne M Schennach [aut] |
Maintainer: | Xinyu DAI <xinyu_dai at brown.edu> |
License: | GPL (≥ 3) |
URL: | https://doi.org/10.1093/restud/rdz065 |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | rbbnp results |
Reference manual: | rbbnp.pdf |
Package source: | rbbnp_0.1.0.tar.gz |
Windows binaries: | r-devel: rbbnp_0.1.0.zip, r-release: rbbnp_0.1.0.zip, r-oldrel: rbbnp_0.1.0.zip |
macOS binaries: | r-release (arm64): rbbnp_0.1.0.tgz, r-oldrel (arm64): rbbnp_0.1.0.tgz, r-release (x86_64): rbbnp_0.1.0.tgz, r-oldrel (x86_64): rbbnp_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.